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Identifying Important Nodes in Complex Networks Based on Multiattribute Evaluation

机译：基于多属性评估的复杂网络中重要节点识别

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摘要

Assessing and measuring the importance of nodes in a complex network are of great theoretical and practical significance to improve the robustness of the actual system and to design an efficient system structure. The classical local centrality measures of important nodes only take the number of node neighbors into consideration but ignore the topological relations and interactions among neighbors. Due to the complexity of the algorithm itself, the global centrality measure cannot be applied to the analysis of large-scale complex network. The k-shell decomposition method considers the core node located in the center of the network as the most important node, but it only considers the residual degree and neglects the interaction and topological structure between the node and its neighbors. In order to identify the important nodes efficiently and accurately in the network, this paper proposes a local centrality measurement method based on the topological structure and interaction characteristics of the nodes and their neighbors. On the basis of the k-shell decomposition method, the method we proposed introduces two properties of structure hole and degree centrality, which synthetically considers the nodes and their neighbors' network location information, topological structure, scale characteristics, and the interaction between different nuclear layers of them. In this paper, selective attacks on four real networks are, respectively, carried out. We make comparative analyses of the averagely descending ratio of network efficiency between our approach and other seven indices. The experimental results show that our approach is valid and feasible.
机译：评估和测量复杂网络中节点的重要性对于提高实际系统的鲁棒性和设计有效的系统结构具有重要的理论和实践意义。重要节点的经典局部中心度度量仅考虑节点邻居的数量，而忽略邻居之间的拓扑关系和交互。由于算法本身的复杂性，全局中心度度量不能应用于大规模复杂网络的分析。 k-shell分解方法将位于网络中心的核心节点视为最重要的节点，但仅考虑残差程度，而忽略了节点及其邻居之间的相互作用和拓扑结构。为了有效，准确地识别网络中的重要节点，提出了一种基于节点及其邻居的拓扑结构和交互特性的局部中心度测量方法。在k-shell分解方法的基础上，提出了结构孔和度中心性两个属性，综合考虑了节点及其邻居的网络位置信息，拓扑结构，尺度特性以及不同核之间的相互作用。他们的层。在本文中，分别对四个真实网络进行选择性攻击。我们对我们的方法与其他七个指标之间的网络效率平均下降比率进行了比较分析。实验结果表明，该方法是有效可行的。

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  • 来源
    《Mathematical Problems in Engineering》 |2018年第6期|8268436.1-8268436.11|共11页
  • 作者

    Xu Hui; Zhang Jianpei; Yang Jing; Lun Lijun;

  • 作者单位

    Harbin Engn Univ, Coll Comp Sci & Technol, Harbin, Heilongjiang, Peoples R China;

    Harbin Engn Univ, Coll Comp Sci & Technol, Harbin, Heilongjiang, Peoples R China;

    Harbin Engn Univ, Coll Comp Sci & Technol, Harbin, Heilongjiang, Peoples R China;

    Harbin Normal Univ, Coll Comp Sci & Informat Engn, Harbin, Heilongjiang, Peoples R China;

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